Convergence and No-Regret in Multiagent Learning
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[1] L. Shapley,et al. Stochastic Games* , 1953, Proceedings of the National Academy of Sciences.
[2] G. M. Korpelevich. The extragradient method for finding saddle points and other problems , 1976 .
[3] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.
[4] Nicolò Cesa-Bianchi,et al. Gambling in a rigged casino: The adversarial multi-armed bandit problem , 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.
[5] S. Hart,et al. A simple adaptive procedure leading to correlated equilibrium , 2000 .
[6] H. Kuhn. Classics in Game Theory , 1997 .
[7] Craig Boutilier,et al. The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.
[8] Michael P. Wellman,et al. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.
[9] Yishay Mansour,et al. Nash Convergence of Gradient Dynamics in General-Sum Games , 2000, UAI.
[10] Gunes Ercal,et al. On No-Regret Learning, Fictitious Play, and Nash Equilibrium , 2001, ICML.
[11] Leslie Pack Kaelbling,et al. Playing is believing: The role of beliefs in multi-agent learning , 2001, NIPS.
[12] Manuela M. Veloso,et al. Multiagent learning using a variable learning rate , 2002, Artif. Intell..
[13] Yoav Shoham,et al. Multi-Agent Reinforcement Learning:a critical survey , 2003 .
[14] Keith B. Hall,et al. Correlated Q-Learning , 2003, ICML.
[15] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.
[16] Bikramjit Banerjee,et al. Unifying Convergence and No-Regret in Multiagent Learning , 2005, LAMAS.